The exponential growth of information available to users of various information networks (e.g., broadcast, satellite, or cable television; wide area networks such as the World Wide Web or the Internet), requires organizing the presentation of the available information in an efficient and effective manner. Collaborative filtering attempts to organize presentation of information to a user in a wide area network (e.g., the World Wide Web) based on automatically predicting the interests of a user by establishing relationships between items of interest to the user (e.g., items recently viewed by the user at a commercial website) and other items that have been determined as of interest to other users. Item-based collaborative filtering, illustrated for example at the website “amazon.com” (users who bought x also bought y) is based on the premise that if a number of users purchase both items “x” and “y”, then another user viewing (or purchasing) the item “x” also may be interested in the item “y”.
Other examples of filtering content include human directed programming (e.g., conventional network television programming), demographic based targeting that classifies individuals according to demographics, content based targeting (e.g., Google AdSense available on the World Wide Web at the website address “google.com/adsense”), user defined filters (e.g., a TiVo® WishList search on a commercially-available TiVo® Digital Video Recorder), popularity based targeting, domain-specific knowledge recommendation systems (e.g., available at the website address “pandora.com”) and ratings-based filtering (e.g., a ratings system provided by the online service “Netflix” at the website “netflix.com”).